Evidence map›Paper›PMID 31729955›Full record

ArticleBMC bioinformatics2019

omicplotR: visualizing omic datasets as compositions.

Daniel J Giguere, Jean M Macklaim, Brandon Y Lieng, Gregory B Gloor

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daniel J GiguereDepartment of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, N6A5C1, Canada. dgiguer@uwo.ca.ORCID http://orcid.org/0000-0002-4215-9859
Jean M MacklaimDepartment of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, N6A5C1, Canada.
Brandon Y LiengDepartment of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, N6A5C1, Canada.
Gregory B GloorDepartment of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, N6A5C1, Canada.

Funding

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada RGPIN-03878-2015
6 · The paper itself

Abstract

backgroundDifferential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method.

resultsomicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset.

conclusionsomicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.

Indexed as

Databases as TopicGenomicsSoftwareHigh-Throughput Nucleotide SequencingHumansPrincipal Component AnalysisCompositional dataData visualizationDifferential abundanceDifferential expressionEffect plotsExploratory data analysis

Identifiers

PMID31729955
PMCPMC6858670

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.